Parallel optical neural network system based on micro-cavity optical frequency comb and identification method

Abstract

The invention provides a parallel optical neural network system based on a micro-cavity optical frequency comb and an identification method, and solves the problems that an existing optical neural network system is high in cost, large in size, unfavorable for monolithic integration and the like. The parallel optical neural network system based on the micro-cavity optical frequency comb comprises anarrow linewidth laser, a micro-cavity optical soliton optical frequency comb unit, an optical Bragg grating, an optical amplifier, a wavelength division multiplexing module, a first modulator and anoptical neural network unit which are arranged in sequence. According to the system, the micro-cavity optical soliton optical frequency comb is combined with the optical neural network, the micro-cavity optical soliton optical frequency comb has hundreds of frequency components, hundreds of different characteristics can be identified in parallel, and future application can be met.

Description

technical field [0001] The invention relates to an optical neural network system, in particular to a parallel optical neural network system and an identification method based on a microcavity optical frequency comb. Background technique [0002] With the development of society, data processing is increasing day by day, which leads to an increasing demand for high-speed, real-time, crosstalk-free, and multi-functional AI chips. The functions of AI chips mainly include the joint action of matrix multiplication and nonlinear layers. The propagation speed of traditional electrical chips depends on electron propagation, and there are bottlenecks that are difficult to break through in terms of high-speed matrix multiplication and large-capacity matrix multiplication. The optical neural network chip, whose transmission speed is based on the speed of light, has the natural advantages of fast processing speed, high computing power, and low energy consumption. However, the current pa...

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Problems solved by technology

[0003] Aiming at the problems of high cost, large volume, and unfavorable monolithic integration in the existing optical neural network system, the present invention provides a parallel optical neural network system and identification method based on a microcavity optical frequency comb. The system is simple to operate and compatible with Strong, can efficiently meet the requirements of parallel optical neural network

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